Securing AI Applications: Best Practices for AI Software Development Cybersecurity

AI is one of the hottest trends in the modern software development domain. AI algorithms may differ from simple chatbots that handle basic communication to more complex AI-powered solutions. For example, AI scripts support complex matching algorithms and analytical app features. What is really important is that AI keeps revolutionizing different industries, such as healthcare, fintech, edtech, etc. While the future seems bright with automation and informed decision-making enhanced with AI algorithms, almost every innovation brings new challenges to the table. 

There are still many concerns regarding the security of AI-powered applications. As AI becomes more prevalent, ensuring the security of AI applications is paramount. AI-powered apps are exposed to common application security threats, as well as more specific cybersecurity challenges. Fortunately, there are tried and trusted practices helping companies cope with these problems. In this article, we will explore the importance of keeping AI applications secure as well as the cybersecurity threats they face and best practices for securing your AI software development.

Cybersecurity in AI software development: Why is it important?

The negative impact of cybersecurity threats often seems evident. However, this question may need additional insight. The more businesses know about cybersecurity threats, the better their chances are to cope with security challenges. So, the main threats prompting companies to prioritize AI security include:

• Financial losses. Cyberattacks such as ransomware, data breaches, and financial fraud can result in direct financial losses. In addition, if AI algorithms are engaged in complex calculations or decision-making, their disruptions can have a critical impact. The loss of revenue due to downtime or disruption of critical business operations is a very common case. 
• Reputational damage. Cybersecurity incidents have a significant impact on the reputation and brand image of a business. This is especially relevant to AI security breaches resulting in the leaks of sensitive data. Customers may perceive businesses that fail to protect their sensitive information as untrustworthy or incompetent. This leads to a massive loss of credibility and competitive advantage. 
• Legal and regulatory consequences. Businesses that fail to secure their partners’ intellectual property or sensitive data often face legal consequences. The punishment depends on the nature of the incident and the responsible regulatory body. It may involve restrictions, reputation losses, significant fines, or even legal prosecution of particular individuals. Speaking of fines, the violation of HIPAA data protection rules can lead a company to a fine reaching up to $16 million. 
• Operational disruptions. Cybersecurity threats can disrupt critical business operations, causing downtime, productivity losses, and supply chain disruptions. Moreover, the impact of operational disruptions extends beyond immediate financial losses. In the long run, it affects employee productivity, customer service levels, and overall continuity of business operations.  
• Intellectual property theft. Cyberattacks targeting intellectual property (IP) and proprietary information can have serious implications for businesses. This is especially relevant to the domain of AI software development. There are many experimental and promising scripts and algorithms in this sphere. Having an innovative AI technology stolen due to a security vulnerability is a great failure. In such cases, businesses lose a potential competitive advantage and suffer severe damage to their reputation. 

AI application for cybersecurity threats

It is challenging to find a technology domain that develops faster than the cybersecurity sphere. New cybersecurity threats and malware appear regularly. Meanwhile, tools and practices responding to these threats also appear at an impressive pace. Here is the list of the most common AI application for cybersecurity threats.

• Adversarial attacks: Adversarial attacks exploit vulnerabilities in AI systems by injecting specially crafted inputs to deceive or manipulate the model’s output. These attacks are often targeted at machine learning algorithms used for malware detection, intrusion detection, and other security applications.
• Data poisoning: Data poisoning attacks involve manipulating training data to make AI models rely on poor information. Attackers may inject malicious data or modify existing data to influence the behavior of the model and evade detection.
• Model inversion: Model inversion attacks aim to reverse-engineer AI models to extract sensitive information or proprietary algorithms. The key point here is analyzing the model’s inputs and outputs. Such an approach helps the attackers infer information about the training data or the underlying decision-making process.
• Membership inference: Membership inference attacks target AI models trained on sensitive data. Attackers exploit the model’s behavior to infer whether a particular data point was part of the training dataset, compromising privacy and confidentiality.
• Evasion attacks: AI apps also include AI-powered safeguards. However, evasion attacks aim to bypass these safeguards, such as spam filters, intrusion detection systems, and malware classifiers. Attackers may craft malicious inputs to infiltrate target systems undetected.
• Generative adversarial networks (GANs): GANs are a type of AI model used to generate synthetic data that mimics real data distributions. GANs can be used by attackers to generate realistic-looking but malicious content, such as fake images or phishing emails.
• Model stealing: Model stealing attacks involve extracting or replicating AI models. It is a very common case of intellectual property theft. This problem can cause companies to lose a competitive advantage or suffer reputation losses. 
• Privacy violations: AI-powered cybersecurity systems may inadvertently disclose sensitive information about individuals. Privacy violations can occur due to poor data anonymization. Insufficient access controls in AI software development is also a common problem. 

Overall, there is a great range of AI cybersecurity threats. Diverse malware and custom scripts can bypass even the best encryption protocols. This causes great damage to particular AI-powered apps and the entire AI software development domain. Fortunately, there are adequate responses to these problems. 

Best security practices for AI software development

Now, let’s dive into the main practical part of this article. These best practices for AI software cybersecurity have been tried and tested by industry-leading security specialists. Following these guidelines helps keep your AI software development project on the safe side.

Ensure secure data handling

Focus on the most robust data security measures to protect sensitive data used by AI software. Encrypt data both in transit and at rest to create a powerful barrier against malware. Don’t forget to use secure storage solutions with access controls to restrict data access to authorized users. You may rely on the best security practices offered by the world’s biggest cloud providers. Or you can go with a private cloud solution, allowing you to implement custom security practices. While this approach to storing your data has drawbacks in terms of manageability, it allows you to stay on top of your data security infrastructure. Also, make sure to minimize the collection and retention of unnecessary data. This will help you reduce exposure to privacy risks and comply with regulatory requirements.

Focus on model security

Harden AI models against cybersecurity threats, such as adversarial attacks, data poisoning, and model inversion. Establish robust testing practices and run the tests regularly. This will help you assess your test models for robustness and resilience against attacks. During the tests, implement various techniques, such as input sanitization, adversarial training, and model ensembling. Make sure that the results of all tests and the critical data about your model are documented. This will help your teams stay on top of model assumptions, limitations, and potential vulnerabilities. 

Keep all steps of the software development lifecycle secure

Be sure to integrate security into every stage of the AI software development lifecycle. Focus on security practices from requirements gathering until the stages of deployment and maintenance. Conduct security reviews, code analysis, and penetration testing to identify and address security weaknesses early in the development process. Be sure that your software developers understand and apply the best safety practices while coding. Almost every technology has its list of best security practices that can be found online. However, make sure that there is a senior developer supervising the entire process. 

Implement strong authentication and authorization

Strong authentication and authorization mechanics are vital in all stages of an AI software development lifecycle. Tried and trusted practices are multi-factor authentication, role-based access control, and clearly defined access policies. This will secure you from unauthorized access or misuse of resources. Practice shows that a lack of clear access policies can lead to chaos in AI app security. That’s why you should never underestimate the value of compliance and well-organized rules. 

Ensure secure deployment and configuration

Securely deploy AI software in production environments. During this stage, focus on the best practices for system configuration, network security, and access controls. Rely on tried and trusted secure deployment patterns. For example, containerization and virtualization will allow you to isolate AI components and minimize the attack surface. Monitor and audit system configurations with corresponding tools regularly. This will help you detect and mitigate security issues before they affect your environment.

Focus on continuous monitoring and incident response

Use off-the-shelf solutions or custom scripts to establish real-time monitoring and anomaly detection. This will help you keep your data reliable and, once an issue occurs, track it down to a source table or pipeline. This will help you track all failed activities or deviations from expected behavior in AI software. Establish incident response procedures and protocols to respond to security incidents quickly. With the right approach, your data teams will solve the problems within minutes, long before the issues affect the system or reach the end user. Regularly review and update incident response plans to address emerging threats and vulnerabilities.

Identify risks and establish risk mitigation practices

Assess and mitigate security risks associated with third-party AI software vendors and service providers. Focus only on vendors that adhere to the best security practices. Be sure to review whether they comply with industry standards. As additional safeguards, establish security-oriented agreements. For example, service level agreements (SLAs) will help you clarify security responsibilities and expectations. Also, your monitoring system should monitor all parts of your infrastructure. This means monitoring the third-party integrations and scripts applied by your vendors. 

Train your employees

The human factor is a common source of many security issues. That is why you should provide comprehensive cybersecurity training to employees, contractors, and stakeholders. All the people involved should understand the value of data privacy and the best AI security practices. Inform them about common security threats, best practices for secure development and deployment, and organizational security policies and procedures. Also, create security workflows that will encourage the stakeholders to report any safety vulnerabilities promptly. 

Research the regulations

Failure to comply with safety and data protection regulations can lead companies to significant fines. It is crucial to ensure compliance with applicable laws, regulations, and industry standards related to cybersecurity and data protection. Some examples of regulations to consider include GDPR, HIPAA, and PCI DSS. There are certain practices for each regulation. If needed, consult with certified specialists. Conduct regular audits and assessments to verify compliance with security standards. This will also help you demonstrate due diligence to regulators and auditors.

Establish post-incident analysis

Conduct post-incident analysis and lessons learned sessions. This will help you understand the cause of each incident and identify areas for improvement. Well-thought analysis will help you update security controls and procedures, as well as enhance incident response capabilities. If needed, involve security consulting experts. Even though such an approach may be costly, it will definitely pay you off in the future. AI security experts will help you avoid many critical mistakes that can have a dramatic impact on your AI models. 

Final thoughts

In the rapidly developing domain of AI software development, security is crucial. Failure to keep your AI-powered apps safe can lead you to financial losses, be damaging to your reputation, and bring legal issues. Besides, such issues can cause operational disruptions and intellectual property theft. While there are many cybersecurity threats and malware types, there are also adequate responses to all these issues:

• Ensure secure data handling;
• Focus on AI model security; 
• Maintain security throughout all stages of the SDLC, 
• Implement secure authorization policies; 
• Ensure secure deployment and configuration;
• Establish continuous monitoring and risk management practices;
• Train your employees,
• Research regulations;
• Focus on post-incident analysis. 

Learn how we helped Veritone build the first AI operating system in our case study, and feel free to contact us if you need a team of AI developers for your project.